smile.feature

Class Scaler

java.lang.Object

smile.feature.Scaler

All Implemented Interfaces:

java.io.Serializable, FeatureTransform

Direct Known Subclasses:

WinsorScaler

public class Scaler
extends java.lang.Object
implements FeatureTransform

Scales all numeric variables into the range [0, 1]. If the dataset has outliers, normalization will certainly scale the "normal" data to a very small interval. In this case, the Winsorization procedure should be applied: values greater than the specified upper limit are replaced with the upper limit, and those below the lower limit are replace with the lower limit. Often, the specified range is indicate in terms of percentiles of the original distribution (like the 5th and 95th percentile).

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Scaler(smile.data.type.StructType schema, double[] lo, double[] hi) Constructor.

Method Summary

Modifier and Type	Method and Description
static Scaler	fit(smile.data.DataFrame data) Learns transformation parameters from a dataset.
static Scaler	fit(double[][] data) Learns transformation parameters from a dataset.
java.lang.String	toString()
smile.data.DataFrame	transform(smile.data.DataFrame data) Transform a data frame.
double[]	transform(double[] x) Transform a feature vector.
smile.data.Tuple	transform(smile.data.Tuple x) Transform a feature vector.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface smile.feature.FeatureTransform

transform

Constructor Detail

Scaler

public Scaler(smile.data.type.StructType schema,
              double[] lo,
              double[] hi)

Constructor.

Parameters:

schema - the schema of data.

lo - the lower bound.

hi - the upper bound.

Method Detail

fit

public static Scaler fit(smile.data.DataFrame data)

Learns transformation parameters from a dataset.

Parameters:

data - The training data.

fit

public static Scaler fit(double[][] data)

Learns transformation parameters from a dataset.

Parameters:

data - The training data.

transform

public double[] transform(double[] x)

Description copied from interface: FeatureTransform

Transform a feature vector.

Specified by:

transform in interface FeatureTransform

Parameters:

x - a feature vector.

Returns:

the transformed feature value.

transform

public smile.data.Tuple transform(smile.data.Tuple x)

Description copied from interface: FeatureTransform

Transform a feature vector.

Specified by:

transform in interface FeatureTransform

Parameters:

x - a feature vector.

Returns:

the transformed feature value.

transform

public smile.data.DataFrame transform(smile.data.DataFrame data)

Description copied from interface: FeatureTransform

Transform a data frame.

Specified by:

transform in interface FeatureTransform

Parameters:

data - a data frame.

Returns:

the transformed data frame.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object